Optimizing Data Transmission System Performance in Underwater Wireless Sensor Networks Using the Search and Rescue Algorithm

Underwater Wireless Sensor Networks (UWSNs) have emerged as essential infrastructures for applications such as disaster management, environmental monitoring, and industrial underwater inspections. These networks consist of low-cost, resource-constrained floating sensor nodes operating in deep-sea environments. UWSNs face unique challenges, including limited bandwidth, high underwater pressure, and high error probability. Furthermore, maintaining node positions and managing energy consumption due to dynamic topologies and 3D deployment complicate their operation. To address these challenges, this study proposes an energy-efficient clustering-based routing method enhanced by a chaotic search and rescue evolutionary algorithm. The approach adjusts node depth, replaces low-energy nodes with higher-energy ones, and balances energy consumption through multi-hop data transmission. The simulation, conducted in a 10 × 10 × 10 km 3D underwater environment with water currents of 1–3 m/s, involved 100–500 sensors. Each sensor had 100 J of energy, 2 km communication range, and 200-byte packets. Performance was assessed using PDR, AEC, delay, dead nodes, and throughput across varying densities and data rates. The proposed method outperformed nine existing protocols, achieving the highest PDR, lowest energy use, and best overall efficiency. Simulation results demonstrate improvements in packet delivery and reception rates, reduced energy consumption, and increased network lifespan, indicating the proposed method’s reliability and effectiveness. Managers should prioritize higher sensor densities to enhance performance and energy efficiency in underwater networks. Adaptive data rate strategies can further improve throughput and reduce communication delays. These insights support cost-effective, reliable deployments for long-term underwater monitoring.